Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Go @ 7
Kubernetes @ 4
Linux @ 4
IaC @ 4
Terraform @ 7
Python @ 7
GCP @ 4
Distributed Systems @ 4
Machine Learning @ 4
AWS @ 4
Azure @ 4
Communication @ 4
Networking @ 3
Rust @ 7
GPU @ 4
AI @ 4
InfiniBand @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. Node Infra owns the full lifecycle of accelerator capacity at Anthropic: ingesting and provisioning compute from major CSPs and internal datacenters, standing up and scaling clusters from thousands to hundreds of thousands of hosts, and building health, diagnostics and repair automation for GPU, TPU and Trainium nodes.
Responsibilities
- Own the technical strategy and roadmap for node lifecycle management: ingestion, bring-up, health checking, and automated repair
- Drive cross-team initiatives to build and scale AI clusters across multiple clouds and accelerator families
- Design and operate systems that detect, isolate, and remediate unhealthy hardware automatically to improve fleet MTBI and minimize stranded capacity
- Define infrastructure architecture and solve the hardest problems directly or by working through others
- Collaborate with cloud providers and internal research/inference/product teams on long-term compute, data, and infrastructure strategy
- Establish and evolve operational excellence practices including incident response, postmortems, and on-call
- Mentor and coach engineers to support their growth
Requirements
Minimum qualifications
- Deep expertise in distributed systems, reliability, and cloud platforms (examples: Kubernetes, IaC, AWS/GCP/Azure)
- Strong proficiency in at least one systems language (examples: Rust, Go, Python) and IaC proficiency with Terraform
- Hands-on experience with machine learning accelerators (GPUs, TPUs, or Trainium)
- Track record of leading complex, multi-quarter technical initiatives spanning multiple teams or systems
- Ability to build alignment across senior stakeholders and communicate effectively at all levels
Preferred qualifications
- 8+ years of software engineering experience, including time as a technical lead
- Experience managing large-scale compute infrastructure at hyperscale (10K+ nodes), including capacity management and efficiency
- Depth in one or more of: Kubernetes internals (scheduler, autoscaler, kubelet, Karpenter), cluster orchestration systems (Mesos, Borg-like), or node provisioning pipelines
- Low-level systems experience: kernel, virtualization, device drivers, firmware, or hardware health/diagnostics daemons
- Familiarity with high-performance networking (EFA, RDMA, InfiniBand) for distributed ML workloads
- Demonstrated ownership of production reliability for high-throughput, latency-sensitive systems
- Contributions to relevant open-source projects (Kubernetes, Linux kernel, container runtimes, etc.)
- Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems
Compensation
Annual Salary: £325,000 - £485,000 GBP
Logistics
- Minimum education: Bachelor’s degree or equivalent combination of education/training/experience
- Location-based hybrid policy: staff expected to be in one of our offices at least 25% of the time (some roles may require more)
- Visa sponsorship: Anthropic states they sponsor visas and retain an immigration lawyer to assist, though not every role/candidate may be eligible
How we're different
Anthropic works as a cohesive team on a few large-scale research efforts, values communication, and focuses on high-impact AI research with collaborative research discussions to align work with long-term goals.
Benefits
Anthropic offers competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office space for collaboration.